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Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry

Xiao-Kang Lun^1,2, Vito RT Zanotelli^1,3, James D Wade^1,4, Denis Schapiro^1,3, Marco Tognetti^1,2,5, Nadine Dobberstein¹ & Bernd Bodenmiller¹

¹Institute of Molecular Life Sciences, University of Zürich, Zürich, Switzerland; ²Molecular Life Science Ph.D. Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, Zürich, Switzerland; ³Systems Biology Ph.D. Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, Zürich, Switzerland; ⁴Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America; ⁵Institute of Biochemistry, ETH Zürich, Zürich, Switzerland

Signaling networks are key regulators of cellular function. Although the concentrations of signaling proteins are perturbed in disease states, such as cancer, and are modulated by drug therapies, our understanding of how such changes shape the properties of signaling networks is limited. Here we couple mass-cytometry-based single-cell analysis with overexpression of tagged signaling proteins to study the dependence of signaling relationships and dynamics on protein node abundance. Focusing on the epidermal growth factor receptor (EGFR) signaling network in HEK293T cells, we analyze 20 signaling proteins during a 1-h EGF stimulation time course using a panel of 35 antibodies. Data analysis with BP-R2, a measure that quantifies complex signaling relationships, reveals abundance-dependent network states and identifies novel signaling relationships. Further, we show that upstream signaling proteins have abundance-dependent effects on downstream signaling dynamics. Our approach elucidates the influence of node abundance on signal transduction networks and will further our understanding of signaling in health and disease.

Reference:

Lun, X.-K. et al. Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry. Nat. Biotechnol. (2017). doi:10.1038/nbt.3770